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Course Detail

Learning Objectives

  • LO1: To introduce characteristics of biomedical signals
  • LO2: To provide understanding of artifact removal in biomedical signals
  • LO3: To enhance knowledge in event detection and waveform analysis of biomedical signals
  • LO4: To provide insight on pattern classification in biomedical signals

Course Outcomes

  • CO1: Ability to understand concepts of signal processing
  • CO2: Ability to apply algorithms for signal processing
  • CO3: Ability to analyse biomedical signals and systems
  • CO4: Ability to evaluate biomedical signal processing systems

Course Contents

Brief introduction to biomedical signals – Challenges in biomedical signal acquisition and analysis – Need for Computer Aided Diagnosis (CAD)

Sampling and reconstruction – Types of noise – Random noise – Structured noise – Physiological interference – Linear time-invariant filters – Time domain filters – Synchronized averaging – Moving average filters – Derivative based filters

Transform domain analysis of signals and systems – Discrete Fourier Transform (DFT) and its properties – Pole-zero plot – Time-frequency analysis – Short-Time Fourier Transform (STFT) – Wavelet Transform Filter design – Butterworth filters – Notch and comb filters – Event detection – Analysis of waveshape and waveform complexity – Morphological analysis – Envelope extraction and analysis – Feature extraction – Receiver operating characteristics – Case studies – Removal of artifacts – QRS Detection and classification of ectopic beats in ECG signals – Detection of epileptic seizures in EEG signals – Study of muscular contraction using parametric analysis of EMG signals


  1. Rangayyan, Rangaraj M, Biomedical signal analysis, John Wiley & Sons, 2015
  2. Subasi, Abdulhamit. Biomedical signal analysis and its usage in healthcare in Biomedical Engineering and its Applications in Healthcare, pp. 423-452. Springer, 2019.
  3. Devasahayam, S.R., Signals and systems in biomedical engineering: signal processing and physiological systems modeling. Springer Science & Business Media, 2014.
  4. Haykin, Simon, and Barry Van Veen, Signals and systems, John Wiley & Sons, 2007
  5. John G.Proakis and DimitusG.Manolakis, “Digital Signal Processing, Principles, Algorithms and Applications”, Third Edition, Prentice Hall of India, 2002.

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